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Found 12 papers, 2 papers with code
Polite Task-oriented Dialog Agents: To Generate or to Rewrite?
In this paper, we conduct a novel assessment of politeness strategies for task-oriented dialog agents under a transfer learning scenario.
Dwell in the Beginning: How Language Models Embed Long Documents for Dense Retrieval
This study investigates the existence of positional biases in Transformer-based models for text representation learning, particularly in the context of web document retrieval.
GlórIA - A Generative and Open Large Language Model for Portuguese
We introduce Gl\'orIA, a robust European Portuguese decoder LLM.
Large Language Models for Captioning and Retrieving Remote Sensing Images
In this work, we propose RS-CapRet, a Vision and Language method for remote sensing tasks, in particular image captioning and text-image retrieval.
Plan-Grounded Large Language Models for Dual Goal Conversational Settings
In this paper, we tackle a dual goal mixed-initiative conversational setting where the LLM not only grounds the conversation on an arbitrary plan but also seeks to satisfy both a procedural plan and user instructions.
TWIZ-v2: The Wizard of Multimodal Conversational-Stimulus
In this report, we describe the vision, challenges, and scientific contributions of the Task Wizard team, TWIZ, in the Alexa Prize TaskBot Challenge 2022.
Grounded Complex Task Segmentation for Conversational Assistants
Hence, when conversational assistants walk users through the steps of complex tasks, there is a need to structure the task into manageable pieces of information of the right length and complexity.
Rating Prediction in Conversational Task Assistants with Behavioral and Conversational-Flow Features
Predicting the success of Conversational Task Assistants (CTA) can be critical to understand user behavior and act accordingly.
The Wizard of Curiosities: Enriching Dialogues with Fun Facts
Introducing curiosities in a conversation is a way to teach something new to the person in a pleasant and enjoyable way.
Task Conditioned BERT for Joint Intent Detection and Slot-filling
Dialogue systems need to deal with the unpredictability of user intents to track dialogue state and the heterogeneity of slots to understand user preferences.
Knowledge-driven Answer Generation for Conversational Search
In this work, we propose a knowledge-driven answer generation approach for open-domain conversational search, where a conversation-wide entities' knowledge graph is used to bias search-answer generation.
Cross-Modal Subspace Learning with Scheduled Adaptive Margin Constraints
Cross-modal embeddings, between textual and visual modalities, aim to organise multimodal instances by their semantic correlations.
